A question answer system answers questions posed in a natural language format by applying advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies. Question answer systems differ from typical document search technologies because document search technologies return a list of documents ranked in order of relevance to a word query, whereas a question answer system receives a question expressed in a natural language, analyzes the question in a natural language context, and returns a precise answer to the question.
System developers may train question answer systems to specific domains to provide more relevant answers to domain-specific questions (e.g., financial domain, travel domain, etc.). One approach to training a question answer system is to ingest corpora from trusted, traditional sources (textbooks, journals) that include accurate information. The question answer system may ingest structured resources, such as relational databases or spreadsheets, which are designed to facilitate finding relationships between specific entities.
At times, semi-structured resources are available to a question answer system to ingest. However, items in semi-structured resource may be loosely organized and not suitable for the question answer system to query.